Build an Explainable AI Decision Workflow using Python & LangGraph

Description

In this video, we build a real-world AI decision-making system using Python and LangGraph. You will learn how to design a confidence-based workflow where an AI system decides whether a document should be automatically approved or sent for manual review — with complete audit logging and explainability. 🔍 What this video covers: ✔ What is a state-based AI workflow ✔ How confidence scores drive AI decisions ✔ Building decision nodes using LangGraph ✔ Maintaining audit logs for explainable AI ✔ Designing production-ready AI workflows ✔ Real-world use cases (Fraud Detection, Loan Approval, Compliance) 🧠 Problem Statement: Given a document and its confidence score, the system should: - Auto-approve high-confidence cases - Route low-confidence cases for manual review - Log every decision step for transparency ⚙️ Tech Stack Used: - Python - LangGraph - State-based workflow design - Audit logging 💼 Real-World Applications: - AI-powered document approval systems - Fraud detection workflows - HR resume screening - Banking & loan approval systems - Compliance and risk assessment engines 🎯 Who should watch this? - Python Developers - Data Scientists - AI / ML Engineers - Generative AI Engineers - Anyone preparing for AI system design interviews 📌 If you’re preparing for senior AI or Generative AI roles, this video will help you understand how real AI decision systems are built in production. 👍 Like, Share & Subscribe for more real-world AI engineering content!